Visual Data Mining

The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visual information processing. The need for a systematic and methodological development of visual analytics was detected.

This book aims at addressing this need. Through a collection of 21 contributions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications.

Klappentext

Visual Data Mining-Opening the Black Box Knowledge discovery holds the promise of insight into large, otherwise opaque datasets. Thenatureofwhatmakesaruleinterestingtoauserhasbeendiscussed 1 widely but most agree that it is a subjective quality based on the practical u- fulness of the information. Being subjective, the user needs to provide feedback to the system and, as is the case for all systems, the sooner the feedback is given the quicker it can in?uence the behavior of the system. There have been some impressive research activities over the past few years but the question to be asked is why is visual data mining only now being - vestigated commercially? Certainly, there have been arguments for visual data 2 mining for a number of years - Ankerst and others argued in 2002 that current (autonomous and opaque) analysis techniques are ine?cient, as they fail to - rectly embed the user in dataset exploration and that a better solution involves the user and algorithm being more tightly coupled. Grinstein stated that the "current state of the art data mining tools are automated, but the perfect data mining tool is interactive and highly participatory," while Han has suggested that the "data selection and viewing of mining results should be fully inter- tive, the mining process should be more interactive than the current state of the 2 art and embedded applications should be fairly automated . " A good survey on 3 techniques until 2003 was published by de Oliveira and Levkowitz .

Inhalt

Visual Data Mining: An Introduction and Overview.- Visual Data Mining: An Introduction and Overview.- 1 - Theory and Methodologies.- The 3DVDM Approach: A Case Study with Clickstream Data.- Form-Semantics-Function - A Framework for Designing Visual Data Representations for Visual Data Mining.- A Methodology for Exploring Association Models.- Visual Exploration of Frequent Itemsets and Association Rules.- Visual Analytics: Scope and Challenges.- 2 - Techniques.- Using Nested Surfaces for Visual Detection of Structures in Databases.- Visual Mining of Association Rules.- Interactive Decision Tree Construction for Interval and Taxonomical Data.- Visual Methods for Examining SVM Classifiers.- Text Visualization for Visual Text Analytics.- Visual Discovery of Network Patterns of Interaction between Attributes.- Mining Patterns for Visual Interpretation in a Multiple-Views Environment.- Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships.- Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Data.- Context Visualization for Visual Data Mining.- Assisting Human Cognition in Visual Data Mining.- 3 - Tools and Applications.- Immersive Visual Data Mining: The 3DVDM Approach.- DataJewel: Integrating Visualization with Temporal Data Mining.- A Visual Data Mining Environment.- Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia.- Towards Effective Visual Data Mining with Cooperative Approaches.